Bar Plot of cook's distance to detect observations that strongly influence
fitted values of the model.

ols_plot_cooksd_bar(model)

## Arguments

model |
An object of class `lm` . |

## Value

`ols_plot_cooksd_bar`

returns a list containing the
following components:

outliersa tibble with observation number and `cooks distance`

that exceed `threshold`

threshold`threshold`

for classifying an observation as an outlier

## Details

Cook's distance was introduced by American statistician R Dennis Cook in
1977. It is used to identify influential data points. It depends on both the
residual and leverage i.e it takes it account both the *x* value and
*y* value of the observation.

Steps to compute Cook's distance:

Delete observations one at a time.

Refit the regression model on remaining \(n - 1\) observations

examine how much all of the fitted values change when the ith observation is deleted.

A data point having a large cook's d indicates that the data point strongly influences the fitted values.

## Deprecated Function

`ols_cooksd_barplot()`

has been deprecated. Instead use `ols_plot_cooksd_bar()`

.

## See also

[ols_plot_cooksd_chart()]

## Examples

model <- lm(mpg ~ disp + hp + wt, data = mtcars)
ols_plot_cooksd_bar(model)